SG: Exploring the Impact of Model (Mis) Specification on Empirical Divergence-Time Estimates

系统发育树--对物种间进化关系的估计--已成为系统学、进化生物学、生态学、分子生物学和流行病学几乎所有研究领域的核心，因为它们提供了基本和明确的历史观点。系统发育学已将其影响的分支从科学领域扩展到公共领域，为有关病原体的监测和监测、疫苗设计和保护优先事项的决策提供信息。尽管许多系统发育是基于从现存物种(或菌株)收集的分子序列数据(DNA)，这些树也可以提供关于一个谱系内绝对或相对分支时间的信息。这种时间信息对许多问题至关重要，比如推断一种毒力强的病毒株最初出现的时间，以及估计它变化的速度。这些考虑促使了大量数学模型的发展，用于推断进化树的时间尺度。该项目试图使用经验数据集评估这些数学模型的可靠性，并将向研究人员和公众通报使用这些方法的最佳做法。通过加州大学历史黑人学院和大学倡议和美国戴维斯学生多样性最大化倡议招募的本科生将接受统计系统发生方法和生物信息学方面的培训。所有开发的软件都将在开放源码许可下自由分发。研究人员还将就贝叶斯发散时间估计方法开发一个新的独立研讨会和相关教材，供广泛使用，包括不直接从事系统发育研究的研究人员。这项研究的主要目标是探索贝叶斯方法在经验环境下估计物种发散时间的统计行为。这一主要目标将通过将目前实施的所有松弛时钟模型和校准方法应用于大样本经验数据集来实现：(1)揭示发散时间估计对特定松弛时钟模型/校准方法的敏感程度；(2)探索三个主要模型分量--分支率先验、节点年龄先验和校准方法--对发散时间估计的相对影响；(3)使用稳健的贝叶斯模型比较方法来评估候选松弛时钟模型池与真实数据的相对匹配；以及(4)开发分析协议和实现管道，以自动有效地探索用于实证分析的放松时钟模型空间。促进更仔细的模型选择将提高我们估计分歧时间的能力，这反过来将广泛地造福于科学和更广泛的社区。

Phylogenetic trees - estimates of the evolutionary relationships among species - have become central to virtually all areas of research in systematics, evolutionary biology, ecology, molecular biology, and epidemiology because of the essential and explicit historical perspectives they provide. Phylogenies have extended the branches of their influence from the scientific to the public realm, informing decisions regarding the surveillance and monitoring of pathogens, vaccine design, and conservation priorities. Although many phylogenies are based on molecular sequence data (DNA) collected from extant species (or strains), these trees can also provide information regarding the absolute or relative branching times within a lineage. This temporal information is critical to many questions, such as inferring when a virulent strain of a virus first arose and estimating how quickly it is changing. These considerations have motivated the development of a large number of mathematical models for inferring the time scale of evolutionary trees. This project seeks to assess the reliability of these mathematical models using empirical datasets, and will inform researchers and public alike on the best practices for using these methods. Undergraduates recruited through the University of California Historically Black Colleges and Universities initiative and the US Davis Initiative for Maximizing Student Diversity will be trained in statistical phylogenetic methods and bioinformatics. All software developed will be distributed freely under open-source licenses. The researcher will also develop a new, stand-alone workshop and associated teaching materials on Bayesian divergence-time estimation methods to be used broadly, including by researchers who do not work directly in phylogenetic research.The primary objective of this research is to explore the statistical behavior of Bayesian methods for estimating species divergence times in an empirical setting. This main goal will be achieved by applying all currently implemented relaxed-clock models and calibration methods to a large sample of empirical datasets to: (1) reveal the extent to which divergence-time estimates are sensitive to the specified relaxed-clock model/calibration method; (2) explore the relative influence of the three primary model components - branch-rate priors, node-age priors, and calibration approaches - on divergence-time estimates; (3) assess the relative fit of the pool of candidate relaxed-clock models to real data using robust Bayesian model-comparison methods; and (4) develop analytical protocols and implement pipelines that automate the efficient exploration of relaxed-clock model space for empirical analyses. Facilitating more careful model selection will improve our ability to estimate divergence times, which, in turn, will broadly benefit scientific and broader communities.